Multivariate Analysis of Job Pause Time Data Using Classification and Regression Tree and Kernel Clustering

Abstract

The well treatment program is an important part of the field development plan, and certain variables, such as job pause time (JPT), can affect its efficiency. JPT is the time during which pumping is paused between subsequent treatments of a job. The objectives of this work are to investigate whether, from existing data, it is possible to find patterns in significant variables that affect the extreme values of JPT in a particular region. The answers are sought by applying a classification and regression tree (CART) to both categorical and continuous variables in the database. The practical application of CART is presented using case studies first using classical CART analysis, then using CART analysis with enhancement tools such as the normal score transform (NST), and then dividing the large dataset into smaller groups using clustering. Significant variables are found that affect the response variables, and predictor variables are ranked in order of their importance. Such information can be used to control predictor variables that cause high JPT. The results are outlined in an intuitive way, including categorical, continuous, and missing values. Because CART is a data driven, deterministic model, we cannot calculate the confidence interval of the predicted response. Confidence in the results is purely based on the historical values, and the accuracy of the result produced by a tree model depends on the quality of the recorded data measured in terms of volume, reliability, and consistency. The prediction capability of CART is enhanced by the use of NST and clustering techniques. The approach presented in this paper analyzes a dataset with limited information and high uncertainty and should lead to developing a method for generating proxy models to find future success indices (e.g., for drilling efficiency or production from a fracture). This could standardize stimulation and generate decision ‘best practices’ to save costs in field development and the optimization process.

Multivariate Analysis and Data Mining of Well-Stimulation Data by Use of Classification-and-Regression Tree With Enhanced Interpretation and Prediction Capabilities

“…CART has been applied in several areas, such as the financial industry (Cashin and Dattagupta 2008), manufacturing and marketing (Chen and Su 2008), and medical industries (Snousy et al 2011), and even in weed science (Wiles and Brodahl 2004). Different versions of decision trees have also been applied in the petroleum industry to estimate production profiles along with uncertainty assessments in long-term production forecasts (Jensen 1998); for data classification and partitioning to predict permeability from well logs (Perez et al 2005); for case-based reasoning and planning of the execution of a fracturing job (Popa and Wood 2011); to predict average production of a well from several variables, such as producer, acid volume, and strength (Yarus et al 2006); and to predict the oil production from five significant parameters (permeability, porosity, first shut-in pressure, residual oil, and water saturation) by use of a neural-based decision-tree model (Lee and Yen 2002). Recently, the boosted regression tree was applied to the data from more than 15,000 producing wells in the Barnett shale play to predict maximum gas rates and find the relative importance of the different inputs used in the treatment (Lafollette et al 2012).…”